I rewrote LangChain in 300 lines of Rust (and here's what I found)

LangChain has over 200,000 lines of code. I wanted to understand what RAG actually does — not what LangChain says it does. So I built the whole pipeline from scratch in Rust. 300 lines. No magic. GitHub : LakshmiSravyaVedantham/nano-rag What RAG actually is (in one sentence) You take a document, split it into chunks, turn each chunk into a vector, store those vectors, and when a user asks a question, you find the most similar chunks and feed them to an LLM as context. That's it. That's all of RAG. Here's the complete architecture — 4 files: src/ ├── chunk.rs # Text → overlapping chunks (~40 lines) ├── embed.rs # Chunks → embeddings + cosine sim (~55 lines) ├── store.rs # In-memory vector store + top-K (~50 lines) └── main.rs # CLI: index + query commands (~80 lines) Let me walk through each one. Step 1: Chunking ( chunk.rs ) Before you can embed anything, you need to split your document into pieces small enough to fit in an embedding model's context window. pub fn split_chunks ( text :